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构建脉络膜黑色素瘤免疫相关风险特征。

Construction of immune-related risk signature for uveal melanoma.

机构信息

State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.

School of Public Health, Sun Yat-sen University, Guangzhou, China.

出版信息

Artif Cells Nanomed Biotechnol. 2020 Dec;48(1):912-919. doi: 10.1080/21691401.2020.1773480.

Abstract

Uveal melanoma (UM) is the most frequent primary ocular tumour among adults. Here, we aimed to establish the immune cell-based signature to predict the overall survival (OS) of UM patients. The mRNA profile and matched clinical records of 80 UM patients were downloaded from The Cancer Genome Atlas (TCGA) database. CIBERSORT was used to verify the immune cell types of individuals. The univariate analysis found the CD8+ T cell, monocyte, CD4+ memory T cell (resting) and mast cell (resting) were significantly associated with the OS of UM patients. Subsequently, the LASSO Cox regression test was applied to establish the signature, by which the patients were separated into high- and low-risk subgroups. The Kaplan-Meier analyses found for these patients in the high-risk group had a poor survival rate than those in the low-risk group. The predictive value and stability were confirmed by the receiver operative characteristics curves. Pathway analyses found that the differentially expressed genes between the high- and low-risk subgroups were mainly centralised on immune response-related pathways. Further, the comparison of our signature with clinicopathological records confirmed its superiority and independence. In summary, we established an immune cell-based prognosis-predicting signature for UM patients, which will benefit the individual's treatment.

摘要

葡萄膜黑色素瘤 (UM) 是成年人中最常见的原发性眼部肿瘤。在这里,我们旨在建立基于免疫细胞的特征,以预测 UM 患者的总生存期 (OS)。从癌症基因组图谱 (TCGA) 数据库下载了 80 名 UM 患者的 mRNA 图谱和匹配的临床记录。CIBERSORT 用于验证个体的免疫细胞类型。单因素分析发现 CD8+T 细胞、单核细胞、CD4+记忆 T 细胞(静止)和肥大细胞(静止)与 UM 患者的 OS 显著相关。随后,应用 LASSO Cox 回归检验建立特征,通过该特征将患者分为高风险和低风险亚组。Kaplan-Meier 分析发现,高风险组的这些患者的生存率低于低风险组。通过接受者操作特征曲线确认了预测价值和稳定性。通路分析发现,高风险和低风险亚组之间差异表达的基因主要集中在免疫反应相关通路。此外,我们的特征与临床病理记录的比较证实了其优越性和独立性。总之,我们建立了一个基于免疫细胞的 UM 患者预后预测特征,这将有利于患者的个体化治疗。

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